CVA: Canonical variate analysis for multienvironment and...

In statforbiology: Tools for Data Analyses in Biology

Canonical variate analysis for multienvironment and multitrait genotype experiments

Description

This function performs canonical variate analysis as a descriptive visualisation tool. It is close to the 'lda()' function in the MASS package but it is not meant to be used for discriminant analyses.

Usage

CVA(dataset, groups, scale = TRUE, constraint = 3)

Arguments

dataset	dataset is a multidimensional matrix of observations
groups	groups is a vector coding for groupings
scale	whether the data needs to be standardised prior to analysis. Defaults to TRUE
constraint	It is the type of scaling for eigenvectors, so that canonical variates have: 1 = unit within-group standard deviations (most common); 2 = unit total standard deviations; 3 = unit within group norms; 4 = unit total norms. It defaults to 3

Details

More detail can be found in a blog page, at 'https://www.statforbiology.com/2023/stat_multivar_cva/'. Please, note that preliminary data transformations (e.g.: standardisation) are left to the user and must be performed prior to analyses (see example below).

Value

TOT	matrix of total variances-covariances
B	matrix of 'between-groups' variances-covariances
W	matrix of 'within-group' variances-covariances
B/W	matrix of W^{-1} B
eigenvalues	vector of eigenvalues
eigenvectors	matrix of eigenvectors
proportion	a vector containing the proportion of total discriminating ability captured by each canonical variate
correlation	vector of canonical correlations
squared.canonical.correlation	vector of squared canonical correlations
coefficients	matrix of canonical coefficients
scores	matrix of canonical scores
centroids	matrix of scores for centroids
total.structure	matrix of total canonical structure
between.structure	matrix of between-groups canonical structure
within.structure	matrix of within-groups canonical structure
class.fun	matrix of classifications functions
class.val	matrix of classification values
within.structure	matrix of within-groups canonical structure
class	vector of predicted classes

Author(s)

Andrea Onofri

References

https://www.statforbiology.com/2023/stat_multivar_cva/

Examples

dataset <- getAgroData("WheatQuality4years")
dataset$Year <- factor(dataset$Year)
head(dataset)

# Standardise the data
groups <- dataset$Genotype
Z <- apply(dataset[,3:6], 2, scale, center = TRUE, scale = TRUE)
head(Z)

# Performs CVA
cvaobj <- CVA(Z, groups)
cvaobj

statforbiology documentation built on Oct. 30, 2024, 9:13 a.m.